ITER: Image-to-pixel Representation for Weakly Supervised HSI Classification

Recent years have witnessed the superiority of deep learning-based algorithms in the field of HSI classification. However, a prerequisite for the favorable performance of these methods is a large number of refined pixel-level annotations. Due to atmospheric changes, sensor differences, and complex l...

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Vydáno v:IEEE transactions on image processing Ročník 33; s. 1
Hlavní autoři: Yang, Jiaqi, Du, Bo, Wang, Di, Zhang, Liangpei
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Algorithms
Annotations
Classification
Feature extraction
high frequency-aware
HSI classification
Hyperspectral imaging
Image classification
image-level annotation
Labeling
Labels
Land cover
Machine learning
Pixels
Representations
Supervised learning
Training
Transformers
Weakly supervised learning
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:Recent years have witnessed the superiority of deep learning-based algorithms in the field of HSI classification. However, a prerequisite for the favorable performance of these methods is a large number of refined pixel-level annotations. Due to atmospheric changes, sensor differences, and complex land cover distribution, pixel-level labeling of high-dimensional hyperspectral image (HSI) is extremely difficult, time-consuming, and laborious. To overcome the above hurdle, an Image-To-pixEl Representation (ITER) approach is proposed in this paper. To the best of our knowledge, this is the first time that image-level annotation is introduced to predict pixel-level classification maps for HSI. The proposed model is along the lines of subject modeling to boundary refinement, corresponding to pseudo-label generation and pixel-level prediction. Concretely, in the pseudo-label generation part, the spectral/spatial activation, spectral-spatial alignment loss, and geographic element enhancement are sequentially designed to locate discriminate regions of each category, optimize multi-domain class activation map (CAM) collaborative training, and refine labels, respectively. For the pixel-level prediction portion, a high frequency-aware self-attention in a high-enhanced transformer is put forward to achieve detailed feature representation. With the two-stage pipeline, ITER explores weakly supervised HSI classification with image-level tags, bridging the gap between image-level annotation and dense prediction. Extensive experiments in three benchmark datasets with state-of-the-art (SOTA) works show the performance of the proposed approach.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2023.3326699